Update README.md

README.md

@@ -1,69 +1,94 @@
-Project: PRECISE-GBM - Model training & retraining helpers
-
-Overview
-
-This repository contains code to train models (Gaussian Mixture labelling + SVM and ensemble classifiers) and to persist all artifacts required to reproduce or retrain models on new data. It includes:
-
-- `Scenario_heldout_final_PRECISE.py` — training pipeline producing `.joblib` models and metadata JSONs (selected features, best params, CV results).
-- `retrain_helper.py` — CLI utility to rebuild pipelines, set best params and retrain using saved selected-features and params JSONs. Supports JSON/YAML config files and auto-detection of model type.
-- `README_RETRAIN.md` — detailed retrain examples and a notebook cell.
-
-This repo also includes helper files to make it ready for GitHub:
-- `requirements.txt` — Python dependencies
-- `.gitignore` — recommended ignores (models, caches, logs)
-- `LICENSE` — MIT license
-- GitHub Actions workflow for CI (pytest smoke test)
-
-Getting started (Windows PowerShell)
-
-1) Create and activate a virtual environment
-
-```powershell
-python -m venv .venv
-.\.venv\Scripts\Activate.ps1
-```
-
-2) Install dependencies
-
-```powershell
-pip install --upgrade pip
-pip install -r requirements.txt
-```
-
-3) Run training (note: the training script reads data from absolute paths configured in the script — adjust them or run from an environment where those files are present)
-
-```powershell
-python Scenario_heldout_final_PRECISE.py
-```
-
-The training script will create model files under `models_LM22/` and `models_GBM/` and write metadata JSONs next to each joblib model (selected features, params, cv results) as well as group-level JSON summaries.
-
-Retraining
-
-See `README_RETRAIN.md` for detailed CLI and notebook examples. Short example:
-
-```powershell
-python retrain_helper.py \
-  --model-prefix "models_GBM/scenario_1/GBM_scen1_Tcell" \
-  --train-csv "data\new_train.csv" \
-  --label-col "label"
-```
-
-Notes
-
-- The training script contains hard-coded absolute paths to data files. Before running on another machine, update the `scenarios_*` file paths or place the datasets in the same paths.
-- Retrain helper auto-detects model type when `--model-type` is omitted by looking for `{prefix}_svm_params.json` or `{prefix}_ens_params.json`.
-- YAML config support for retrain requires PyYAML (`pip install pyyaml`).
-
-CI
-
-A basic GitHub Actions workflow runs a smoke pytest to ensure the retrain helper imports and basic pipeline construction works. It does not run heavy training.
-
-Contributing
-
-See `CONTRIBUTING.md` for guidance on opening issues and PRs.
-
-License
-
-This project is released under the MIT License — see `LICENSE`.
-
+<p align="center"> <b> Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma | PRECISE-GBM </b> </p>
+
+<p align="center">
+  <img src="PRECISE-GBM_GUI_logo%20(1).png" alt="PRECISE-GBM Logo">
+</p>
+
+<b> Project: PRECISE-GBM - Model training & retraining helpers </b>
+
+Overview
+
+This repository contains code to train models (Gaussian Mixture labelling + SVM and ensemble classifiers) and to persist all artifacts required to reproduce or retrain models on new data. It includes:
+
+- `Scenario_heldout_final_PRECISE.py` — training pipeline producing `.joblib` models and metadata JSONs (selected features, best params, CV results).
+- `retrain_helper.py` — CLI utility to rebuild pipelines, set best params and retrain using saved selected-features and params JSONs. Supports JSON/YAML config files and auto-detection of model type.
+- `README_RETRAIN.md` — detailed retrain examples and a notebook cell.
+
+This repo also includes helper files to make it ready for GitHub:
+- `requirements.txt` — Python dependencies
+- `.gitignore` — recommended ignores (models, caches, logs)
+- `LICENSE` — MIT license
+- GitHub Actions workflow for CI (pytest smoke test)
+
+Getting started (Windows PowerShell)
+
+1) Create and activate a virtual environment
+
+```powershell
+python -m venv .venv
+.\.venv\Scripts\Activate.ps1
+```
+
+2) Install dependencies
+
+```powershell
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+3) Run training (note: the training script reads data from absolute paths configured in the script — adjust them or run from an environment where those files are present)
+
+```powershell
+python Scenario_heldout_final_PRECISE.py
+```
+
+The training script will create model files under `models_LM22/` and `models_GBM/` and write metadata JSONs next to each joblib model (selected features, params, cv results) as well as group-level JSON summaries.
+
+Retraining
+
+See `README_RETRAIN.md` for detailed CLI and notebook examples. Short example:
+
+```powershell
+python retrain_helper.py \
+  --model-prefix "models_GBM/scenario_1/GBM_scen1_Tcell" \
+  --train-csv "data\new_train.csv" \
+  --label-col "label"
+```
+
+Notes
+
+- The training script contains hard-coded absolute paths to data files. Before running on another machine, update the `scenarios_*` file paths or place the datasets in the same paths.
+- Retrain helper auto-detects model type when `--model-type` is omitted by looking for `{prefix}_svm_params.json` or `{prefix}_ens_params.json`.
+- YAML config support for retrain requires PyYAML (`pip install pyyaml`).
+
+CI
+
+A basic GitHub Actions workflow runs a smoke pytest to ensure the retrain helper imports and basic pipeline construction works. It does not run heavy training.
+
+Contributing
+
+See `CONTRIBUTING.md` for guidance on opening issues and PRs.
+
+License
+
+This project is released under the MIT License — see `LICENSE`.
+
+Citation:
+Please use the following citation when using the repository.
+
+2025
+
+•	 Ghimire P, Modat M, Booth T. Predictive radiogenomic AI Model for patient stratification in brain tumor immunotherapy trials. Neuro-oncology.  Oct 2025; 26(Suppl_3): iii58–iii59. https://doi.org/10.1093/neuonc/noaf193.188
+
+•	 Ghimire P, Modat M, Booth T. Radiogenomic AI model predicts immune status in IDH wildtype glioblastoma: PRECISE-GBM study. RCR open. Jan 2025; 3(1): 100234
+
+2024
+
+•	Ghimire P, Modat M, Booth T. A machine Learning bases predictive radiomics for evaluation of cancer immune signature in glioblastoma: the PRECISE-GBM study. Neuro-Oncology. Oct 2024; 26(suppl_5): v25.
+
+•	Ghimire P, Modat M, Booth T. A radiogenomic machine learning based study to identify Predictive Radiomics for Evaluation of Cancer Immune SignaturE in IDHw Glioblastoma. Neuro-Oncology. Oct 2024; 26(suppl_7): vii3
+
+
+
+
+